Triple

T761549
Position Surface form Disambiguated ID Type / Status
Subject Eswatini E16080 entity
Predicate formerName P65 FINISHED
Object Swaziland E16080 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Swaziland | Statement: [Eswatini, formerName, Swaziland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Swaziland
Context triple: [Eswatini, formerName, Swaziland]
  • A. Eswatini chosen
    Eswatini is a small landlocked monarchy in Southern Africa known for its blend of traditional Swazi culture and modern institutions.
  • B. Lesotho
    Lesotho is a small, landlocked constitutional monarchy in Southern Africa, entirely surrounded by South Africa and known for its mountainous terrain and high-altitude settlements.
  • C. Botswana
    Botswana is a landlocked country in Southern Africa known for its stable democracy, significant diamond resources, and vast wildlife-rich landscapes including the Okavango Delta.
  • D. Zimbabwe
    Zimbabwe is a landlocked country in southern Africa known for its dramatic landscapes, diverse wildlife, and historical sites such as Victoria Falls and the Great Zimbabwe ruins.
  • E. Botswana and Zimbabwe
    Botswana and Zimbabwe are neighboring landlocked countries in Southern Africa that share close historical, economic, and ecological ties.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a493684ee48190bd43b7c78da4aec8 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a682e7d081909c9cd7839a49fb0b completed March 1, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada94e727881909bee9e71a404c95a completed March 8, 2026, 4:52 p.m.
Created at: March 1, 2026, 7:37 p.m.